Change point estimation in monitoring survival time: average of posterior estimates of step change point model parameters

The dataset was collected to model change point estimation in time-to-event data for a clinical process with dichotomous outcomes, death and survival, where patient mix was present. Modelling was completed using a Bayesian framework. The performance of the Bayesian estimators was investigated through simulation in conjunction with RAST CUSUM control charts for monitoring right censored survival time of patients who underwent cardiac surgery procedures within a follow-up period of 30 days.

The dataset presents the average of posterior estimates (mode, sd.) of step change point model parameters ( and ) for a change in the mean survival time following signals (RL) from RAST CUSUM () where and .

该数据集旨在模拟临床过程中二元结果（死亡与存活）的时间至事件数据中的变化点估计，其中患者群体存在多样性。模型构建采用了贝叶斯框架。通过模拟研究贝叶斯估计器的性能，并结合 RAST CUSUM 控制图对患者在 30 天随访期内接受心脏手术术后右端截尾生存时间进行监测。数据集展示了在 RAST CUSUM （）信号（RL）下，平均生存时间发生改变时，步长变化点模型参数（ 和 ）的后验估计值（众数，标准差）。